The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Loss of MicroRNA-192 Promotes Fibrogenesis in Diabetic Nephropathy
Open accessAbstract
The role of microRNAs (miRs), which are endogenous RNA oligonucleotides that regulate gene expression, in diabetic nephropathy is unknown. Here, we performed expression profiling of cultured proximal tubular cells (PTCs) under high-glucose and control conditions. We identified expression of 103 of 328 microRNAs but did not observe glucose-induced changes in expression. Next, we performed miR expression profiling in pooled RNA from formalin-fixed, paraffin-embedded tissue from renal biopsies. We studied three groups of patients with established diabetic nephropathy and detected 103 of 365 miRs. Two miRs differed by more than two-fold between progressors and nonprogressors, and 12 miRs differed between late presenters and other biopsies. We noted the greatest change in miR-192 expression, which was significantly lower in late presenters. Furthermore, in individual biopsies, low expression of miR-192 correlated with tubulointerstitial fibrosis and low estimated GFR. In vitro, treatment of PTCs with TGF-beta1 decreased miR-192 expression. Overexpression of miR-192 suppressed expression of the E-Box repressors ZEB1 and ZEB2, thereby opposing TGF-beta-mediated downregulation of E-cadherin. In summary, loss of miR-192 expression associates with increased fibrosis and decreased estimated GFR in diabetic nephropathy in vivo, perhaps by enhancing TGF-beta-mediated downregulation of E-cadherin in PTCs.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the 2−ΔΔCT Method
Livak, K. J., & Schmittgen, T. D. (2001) · Methods · doi:10.1006/meth.2001.1262 · Cited by 180,860
Park, S.-M., Gaur, A. B., Lengyel, E., & Peter, M. E. (2008) · Genes & Development · doi:10.1101/gad.1640608 · Cited by 2,272
A MicroRNA Signature of Hypoxia
Kulshreshtha, R., Ferracin, M., Wojcik, S. E., Garzon, R., Alder, H., Agosto-Perez, F. J., Davuluri, R., Liu, C.-G., Croce, C. M., Negrini, M., et al. (2007) · Molecular and Cellular Biology · doi:10.1128/mcb.01395-06 · Cited by 1,055
Cited by
TGF-β: the master regulator of fibrosis
Meng, X.-M., Nikolic-Paterson, D. J., & Lan, H. Y. (2016) · Nature Reviews Nephrology · doi:10.1038/nrneph.2016.48 · Cited by 3,528
Renal tubule injury: a driving force toward chronic kidney disease
Liu, B.-C., Tang, T.-T., Lv, L.-L., & Lan, H.-Y. (2018) · Kidney International · doi:10.1016/j.kint.2017.09.033 · Cited by 913
Mechanisms of Tubulointerstitial Fibrosis
Zeisberg, M., & Neilson, E. G. (2010) · Journal of the American Society of Nephrology · doi:10.1681/asn.2010080793 · Cited by 868
TGF-β/Smad signaling in renal fibrosis
Meng, X.-M., Tang, P. M.-K., Li, J., & Lan, H. Y. (2015) · Frontiers in Physiology · doi:10.3389/fphys.2015.00082 · Cited by 666
Renal fibrosis: novel insights into mechanisms and therapeutic targets
Boor, P., Ostendorf, T., & Floege, J. (2010) · Nature Reviews Nephrology · doi:10.1038/nrneph.2010.120 · Cited by 606
Metadata
- Authors
- Krupa, A., Jenkins, R., Luo, D. D., Lewis, A., Phillips, A., & Fraser, D.
- Venue
- Journal of the American Society of Nephrology
- Published
- 2010-03
- Volume
- 21
- Issue
- 3
- Pages
- 438-447
- ISSN
- 1046-6673 (ISSN-L)
- OpenAlex
- W2115280144
- Cited by
- 348
- References
- 3
- FWCI
- 23.62
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Loss of MicroRNA-192 Promotes Fibrogenesis in Diabetic Nephropathy. https://app.cochat.ai/papers/10.1681/asn.2009050530
- The paper
- Krupa, A., Jenkins, R., Luo, D. D., Lewis, A., Phillips, A., & Fraser, D. (2010). Loss of MicroRNA-192 Promotes Fibrogenesis in Diabetic Nephropathy. Journal of the American Society of Nephrology. https://doi.org/10.1681/asn.2009050530
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.